Improving Language Learning Through the Use of Natural Language Processing Improving Language Learning Through the Use of Natural Language Processing
Article Sidebar
Main Article Content
Abstract
This paper examines how Natural Language Processing (NLP) could bring improvement to language learning of primary school students who live in rural areas. NLP has been used in many ways to improve human way of life. Nowadays, in the cities of Thailand many teachers and students already take advantage of the various NLP systems available to aid language learning in their schools. However, in rural areas it is difficult for students and teachers to implement these technological systems. The paper aims to address how NLP could improve language learning in the rural area of Khao Kho district, Phetchabun Province. The study focuses on how the researcher introduced the NLP application to the school in order for it to be used as a tool to improve English language learning in the area of correct pronunciation of English words. The study follows qualitative approach and focuses on primary school students from grades 4 to 6 from Nong Mae Na school. The data was collected from the primary resources in order to identify problems faced by the students in understanding the correct method of pronunciation as well as the obstacles Thai elementary students face in language learning. The population of this study consisted of 60 students categorized into three groups. The DetectMeEnglish application was used for the study, processing the attempts of the sample group and forming correct English pronunciations during the pre-test. Moreover, it was used in various activities though out the course of two days.
The learning activities targeted developing the students’ pronunciation ability and included teaching related to the English Phonics rules, the English final sounds with “s” and “ed” etc. The NLP application was then used again for the post-test to record the improvement of the sample groups English pronunciation. The words used were carefully selected from the English Standard Curriculum set up by the office of Basic Education Commission according to the grades represented in the tests. Nevertheless, each sample group was tested with all the selected words in order to gauge and compare the initial level of understanding and the overall improvement of each group. The results show the effectiveness of the NLP application as a productive resource in language learning for primary school students who lived in rural areas.
Article Details
เนื้อหาและข้อมูลในบทความที่ตีพิมพ์ในวารสารวิชาการคณะมนุษยศาสตร์และสังคมศาสตร์ มหาวิทยาลัยราชภัฏเทพสตรี ถือเป็นข้อคิดเห็นและความรับผิดชอบของผู้เขียนบทความโดยตรง ซึ่งกองบรรณาธิการวารสารไม่จำเป็นต้องเห็นด้วย หรือร่วมรับผิดชอบใดๆ
บทความ ข้อมูล เนื้อหา รูปภาพ ที่ได้รับการตีพิมพ์ในวารสารวิชาการคณะมนุษยศาสตร์และสังคมศาสตร์ มหาวิทยาลัยราชภัฏเทพสตรี ถือเป็นลิขสิทธิ์ของวารสารวิชาการคณะมนุษยศาสตร์และสังคมศาสตร์ มหาวิทยาลัยราชภัฏเทพสตรี หากบุคคลหรือหน่วยงานใดต้องการนำทั้งหมดหรือส่วนหนึ่งส่วนใดไปเผยแพร่ต่าหรือกระทำการใดๆ จะต้องได้รับอนาญาตจากวารสารวิชาการ ฯ ก่อนเท่านั้น
References
Benson, Phil. (2013). Teaching and Researching: Autonomy in Language Learning (2nd ed.). New York: Routledge.
Branden, Van den. (2012). Task-Based Language Education. In: Burns A, Richards JC, The Cambridge Guide to Pedagogy and Practice in Language Teaching. New York: Cambridge University Press.
Burstein, J. (2009). Opportunities for Natural Language Processing Research in Education. In Computational Linguistics and Intelligent Text Processing. Berlin Springer.
Bangkok Post. (2017, May 31). Educational inequality in Thailand: The challenge. Bangkok Post. Retrieved October 10, 2020, from https://www.bangkokpost.com./learning/advanced/1259777.
Eskenazi, M. (2009). An Overview of Spoken Language Tech- nology for Education. Speech Communication, 51(10), 832–844.
Evens, M., &Joel, M. (2006). One-on-one Tutoring by Humans and Computers. New Jersey: Lawrence Erlbaum Associates.
Johnson, Lewis, W. (2007). Serious Use of a Serious Game for Language Learning. In Proceedings of the 13th International Conference on Artipicial Intelligencen in Education, AEID 2017. July 9-13, 2007. Los Angeles, California.
Leacock, C., Chodorow, M., Gamon, M., &Tetreault, J. (2010). Automated Grammatical Error Detection for Language Learners. Synthesis Lectures on Human Language Technologies, 3(1), 1–134.
Mitchell, C., M., Evanini, K., &Zechner, K. (2014). A Trialogue-based Spoken dialogue System for Assessment of English Language Learners. In Proceedings of the International Workshop on Spoken Dialogue Systems, Napa, CA.
Richards, J. C., Rodgers, T. (2001). Approaches and Methods in Language Teaching. (2nd ed.), New York: Cambridge University Press.
Shermis, M. D., &Burstein, J. (2013). Handbook of automated essay evaluation: Current applications and new directions. New York: Routledge.
VanLehn, K. (2011). The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems. Educational Psychologist, 46(4), 197–221.
Wahl, H., Winiwarter, W., &Quirchmayr, G. (2010). Natural Language Processing Technologies for Developing a Language Learning Environment. In Proceedings of the 12th International Conference on Information Integration and Web-based Applications &Services: ACM.